Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Multiplexing and massive parallel sequencing of targeted DNA methylation to predict chronological age

ABSTRACT: Multiplexing and massive parallel sequencing of targeted DNA methylation to predict chronological age

PROVIDER: PRJNA1113996 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR29099500_1.fastq.gz	Fastqsanger.gz
	SRR29099500_2.fastq.gz	Fastqsanger.gz
	SRR29099600_1.fastq.gz	Fastqsanger.gz
	SRR29099600_2.fastq.gz	Fastqsanger.gz
	SRR29099700_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 780

Similar Datasets

Multiplexing and massive parallel sequencing of targeted DNA methylation to predict chronological age

Project description:Estimation of chronological age is particularly informative in forensic contexts. Assessment of DNA methylation status allows for the prediction of age, though the accuracy may vary across models. In this study, we started with a carefully designed discovery cohort with more elderly subjects than other age categories, to diminish the effect of epigenetic drifting. We applied multiplexing and massive parallel sequencing of targeted DNA methylation, which let us to construct a model comprising 25 CpG sites with substantially improved accuracy (MAE=2.279, R=0.92). This model is further validated by an independent cohort (MAE=2.204, 82.7% success (±5 years)). Remarkably, in a multi-center test using trace blood samples from forensic caseworks, the correct predictions (±5 years) are 91.7%. The nature of our analytical pipeline can easily be scaled up with low cost. Taken together, we propose a new age-prediction model featuring accuracy, sensitivity, high-throughput, and low cost. This model can be readily applied in both classic and newly emergent forensic contexts that require age estimation.

2025-03-19 | GSE267985 | GEO

Multiplexing and massive parallel sequencing of targeted DNA methylation to predict chronological age.

Project description:Estimation of chronological age is particularly informative in forensic contexts. Assessment of DNA methylation status allows for the prediction of age, though the accuracy may vary across models. In this study, we started with a carefully designed discovery cohort with more elderly subjects than other age categories, to diminish the effect of epigenetic drifting. We applied multiplexing and massive parallel sequencing of targeted DNA methylation, which let us to construct a model comprising 25 CpG sites with substantially improved accuracy (MAE = 2.279, R = 0.920). This model is further validated by an independent cohort (MAE = 2.204, 82.7% success (±5 years)). Remarkably, in a multi-center test using trace blood samples from forensic caseworks, the correct predictions (±5 years) are 91.7%. The nature of our analytical pipeline can easily be scaled up with low cost. Taken together, we propose a new age-prediction model featuring accuracy, sensitivity, high-throughput, and low cost. This model can be readily applied in both classic and newly emergent forensic contexts that require age estimation.

| S-EPMC11906720 | biostudies-literature

Massive Parallel Sequencing (MPS) of miRNAs in PBMCs

Project description:Massive Parallel Sequencing (MPS) of miRNAs in PBMCs

| PRJNA1062301 | ENA

Massive Parallel Sequencing (MPS) of miRNAs in PBMCs

Project description:We carried out Massive Parallel Sequencing (MPS) to reveal circulating miRNA profiles of 96 patients to distinguish the radiographic and non-radiographic axial spondyloarthritis. The sequencing platform identified 1900 miRNA species, out of which 432 miRNAs were present with the base mean, computed by DESeq2, more than 10. We identified 48 miRNAs, which were different in PBMCs in patients with axial spondyloarthritis compared to healthy controls.

2024-01-09 | GSE252708 | GEO

Cardiomyocyte Dedifferentiation and Proliferation Revealed by Massive Parallel Single-Nucleus Transciprotmic Analysis in Adult Mouse Hearts

Project description:Cardiomyocyte Dedifferentiation and Proliferation Revealed by Massive Parallel Single-Nucleus Transciprotmic Analysis in Adult Mouse Hearts

| PRJNA528299 | ENA

A Multisample Approach in Forensic Phenotyping of Chronological Old Skeletal Remains Using Massive Parallel Sequencing (MPS) Technology.

Project description:It is very important to generate phenotypic results that are reliable when processing chronological old skeletal remains for cases involving the identification of missing persons. To improve the success of pigmentation prediction in Second World War victims, three bones from each of the eight skeletons analyzed were included in the study, which makes it possible to generate a consensus profile. The PowerQuant System was used for quantification, the ESI 17 Fast System was used for STR typing, and a customized version of the HIrisPlex panel was used for PCR-MPS. The HID Ion Chef Instrument was used for library preparation and templating. Sequencing was performed with the Ion GeneStudio S5 System. Identical full profiles and identical hair and eye color predictions were achieved from three bones analyzed per skeleton. Blue eye color was predicted in five skeletons and brown in three skeletons. Blond hair color was predicted in one skeleton, blond to dark blond in three skeletons, brown to dark brown in two skeletons, and dark brown to black in two skeletons. The reproducibility and reliability of the results proved the multisample analysis method to be beneficial for phenotyping chronological old skeletons because differences in DNA yields in different bone types provide a greater possibility of obtaining a better-quality consensus profile.

| S-EPMC10379588 | biostudies-literature

CellTag Indexing: genetic barcode-based sample multiplexing for single-cell genomics

Project description:High-throughput single-cell assays increasingly require special consideration in experimental design, sample multiplexing, batch effect removal, and data interpretation. Here, we describe a lentiviral barcode-based multiplexing approach, CellTag Indexing, which uses predefined genetic barcodes that are also heritable, enabling cell populations to be tagged, pooled, and tracked over time in the same experimental replicate. We demonstrate the utility of CellTag Indexing by sequencing transcriptomes using a variety of cell types, including long-term tracking of cell engraftment and differentiation in vivo. Together, this presents CellTag Indexing as a broadly applicable genetic multiplexing tool that is complementary with existing single-cell technologies.

2019-04-20 | GSE130065 | GEO

PREDICT 2

Project description:PREDICT 2

| PRJEB75460 | ENA

Multiplexing Methods to Reduce Sequencing Costs

Project description:Multiplexing Methods to Reduce Sequencing Costs

| PRJNA761838 | ENA

Multiplexing droplet-based single cell RNA-sequencing using genetic barcodes

Project description:Here, we introduce an in-silico algorithm demuxlet that harnesses naturally occurring genetic variation in a pool of cells from unrelated individuals to discover the sample identity of each cell and identify droplets containing cells from two different individuals (doublets). These two capabilities enable a simple multiplexing design that increases single cell library construction throughput by experimental design where cells from genetically diverse samples are multiplexed and captured at 2-10x over standard workflows. We further demonstrate the utility of sample multiplexing by characterizing the interindividual variability in cell type-specific responses of ~15k PBMCs to interferon-beta, a potent cytokine. Our computational tool enables sample multiplexing of droplet-based single cell RNA-seq for large-scale studies of population variation and could be extended to other single cell datasets that incorporate natural or synthetic DNA barcodes.

2017-06-13 | GSE96583 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data